






"""simple docstring"""





import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
  from transformers.models.roberta.modeling_flax_roberta import (
      FlaxRobertaForCausalLM,
      FlaxRobertaForMaskedLM,
      FlaxRobertaForMultipleChoice,
      FlaxRobertaForQuestionAnswering,
      FlaxRobertaForSequenceClassification,
      FlaxRobertaForTokenClassification,
      FlaxRobertaModel,
  )






class 	a		(   unittest.TestCase						):






    def __init__(			self	:				Optional[Any]			,		__SCREAMING_SNAKE_CASE	:				Any			,		__SCREAMING_SNAKE_CASE	:				List[Any]=13			,		__SCREAMING_SNAKE_CASE	:				str=7			,		__SCREAMING_SNAKE_CASE	:				str=True			,		__SCREAMING_SNAKE_CASE	:				Any=True			,		__SCREAMING_SNAKE_CASE	:				Optional[int]=True			,		__SCREAMING_SNAKE_CASE	:				Optional[int]=True			,		__SCREAMING_SNAKE_CASE	:				Optional[int]=99			,		__SCREAMING_SNAKE_CASE	:				str=32			,		__SCREAMING_SNAKE_CASE	:				List[Any]=5			,		__SCREAMING_SNAKE_CASE	:				Dict=4			,		__SCREAMING_SNAKE_CASE	:				Dict=37			,		__SCREAMING_SNAKE_CASE	:				Tuple="gelu"			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]=0.1			,		__SCREAMING_SNAKE_CASE	:				Dict=0.1			,		__SCREAMING_SNAKE_CASE	:				Tuple=512			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]=16			,		__SCREAMING_SNAKE_CASE	:				Any=2			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]=0.02			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]=4			,		)	->						Tuple:
      lowerCamelCase_       	=		parent
      lowerCamelCase_       	=		batch_size
      lowerCamelCase_       	=		seq_length
      lowerCamelCase_       	=		is_training
      lowerCamelCase_       	=		use_attention_mask
      lowerCamelCase_       	=		use_token_type_ids
      lowerCamelCase_       	=		use_labels
      lowerCamelCase_       	=		vocab_size
      lowerCamelCase_       	=		hidden_size
      lowerCamelCase_       	=		num_hidden_layers
      lowerCamelCase_       	=		num_attention_heads
      lowerCamelCase_       	=		intermediate_size
      lowerCamelCase_       	=		hidden_act
      lowerCamelCase_       	=		hidden_dropout_prob
      lowerCamelCase_       	=		attention_probs_dropout_prob
      lowerCamelCase_       	=		max_position_embeddings
      lowerCamelCase_       	=		type_vocab_size
      lowerCamelCase_       	=		type_sequence_label_size
      lowerCamelCase_       	=		initializer_range
      lowerCamelCase_       	=		num_choices






    def   UpperCamelCase			(			self	:				str   )	->						str:
      lowerCamelCase_       	=		ids_tensor([self.batch_size, self.seq_length]			,		self.vocab_size   )

      lowerCamelCase_       	=		None
      if self.use_attention_mask:
        lowerCamelCase_       	=		random_attention_mask([self.batch_size, self.seq_length]   )

      lowerCamelCase_       	=		None
      if self.use_token_type_ids:
        lowerCamelCase_       	=		ids_tensor([self.batch_size, self.seq_length]			,		self.type_vocab_size   )

      lowerCamelCase_       	=		RobertaConfig(
          vocab_size=self.vocab_size			,		hidden_size=self.hidden_size			,		num_hidden_layers=self.num_hidden_layers			,		num_attention_heads=self.num_attention_heads			,		intermediate_size=self.intermediate_size			,		hidden_act=self.hidden_act			,		hidden_dropout_prob=self.hidden_dropout_prob			,		attention_probs_dropout_prob=self.attention_probs_dropout_prob			,		max_position_embeddings=self.max_position_embeddings			,		type_vocab_size=self.type_vocab_size			,		is_decoder=__SCREAMING_SNAKE_CASE			,		initializer_range=self.initializer_range			,		)

      return config, input_ids, token_type_ids, attention_mask






    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						List[Any]:
      lowerCamelCase_       	=		self.prepare_config_and_inputs()
      lowerCamelCase_	,      lowerCamelCase_	,      lowerCamelCase_	,      lowerCamelCase_       	=		config_and_inputs
      lowerCamelCase_       	=		{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
      return config, inputs_dict






    def   UpperCamelCase			(			self	:				Any   )	->						Optional[Any]:
      lowerCamelCase_       	=		self.prepare_config_and_inputs()
      lowerCamelCase_	,      lowerCamelCase_	,      lowerCamelCase_	,      lowerCamelCase_       	=		config_and_inputs

      lowerCamelCase_       	=		True
      lowerCamelCase_       	=		floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
      lowerCamelCase_       	=		ids_tensor([self.batch_size, self.seq_length]			,		vocab_size=2   )

      return (
          config,
          input_ids,
          token_type_ids,
          encoder_hidden_states,
          encoder_attention_mask,
      )






@require_flax
class 	a		(   __snake_case    , unittest.TestCase						):
    SCREAMING_SNAKE_CASE		:	Any		=					True

    SCREAMING_SNAKE_CASE		:	Tuple		=					(
        (
            FlaxRobertaModel,
            FlaxRobertaForCausalLM,
            FlaxRobertaForMaskedLM,
            FlaxRobertaForSequenceClassification,
            FlaxRobertaForTokenClassification,
            FlaxRobertaForMultipleChoice,
            FlaxRobertaForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )






    def   UpperCamelCase			(			self	:				Tuple   )	->						Union[str, Any]:
      lowerCamelCase_       	=		FlaxRobertaModelTester(self   )






    @slow
    def   UpperCamelCase			(			self	:				Dict   )	->						Tuple:
      for model_class_name in self.all_model_classes:
        lowerCamelCase_       	=		model_class_name.from_pretrained('roberta-base'			,		from_pt=__SCREAMING_SNAKE_CASE   )
        lowerCamelCase_       	=		model(np.ones((1, 1)   )   )
        self.assertIsNotNone(__SCREAMING_SNAKE_CASE   )












"""simple docstring"""





from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest






@require_tokenizers
class 	a		(   __snake_case						):
    SCREAMING_SNAKE_CASE		:	Optional[Any]		=					DistilBertTokenizer
    SCREAMING_SNAKE_CASE		:	Optional[Any]		=					DistilBertTokenizerFast
    SCREAMING_SNAKE_CASE		:	int		=					True






    @slow
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						int:
      lowerCamelCase_       	=		DistilBertTokenizer.from_pretrained('distilbert-base-uncased'   )

      lowerCamelCase_       	=		tokenizer.encode('sequence builders'			,		add_special_tokens=__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		tokenizer.encode('multi-sequence build'			,		add_special_tokens=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

      assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
      assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
          tokenizer.sep_token_id
      ]




